Past events demonstrate a desire for technologies that provide timely identification and geo-location of objects lost within a vast geographic area covering thousands of square kilometers over both land and water.
As one example, on May 8, 2014, a Malaysian Boeing 777-200ER airliner, traveling from Kuala Lumpur to Beijing China, disappeared in flight. A massive search was conducted by multiple countries using advanced satellite, radar, and ISR (Intelligence, Surveillance and Reconnaissance) technologies. However, the airliner was not located within the first two weeks of its disappearance. During this two week period, over 3 million people, both military and civilians, reviewed over 257 million images covering 24,000 sq. kms., including land and water-based images. Even with this manpower and technology, the missing airliner could not be located.
Conventionally, analysts use advanced satellite, radar, and ISR capabilities to collect mission data, but then spend significant time and resources processing these data to extract relevant information. Corporations, such as IBM and Google, that are involved in the processing of large data sets (conventionally known as “Big Data”) are working on the problem for non-image content.
Another approach for analyzing millions of images is to use crowd sourcing via the Internet. However, this approach has drawbacks as well, such as accessibility to up-to-dated satellite imagery, lack of training, and the potential for false reporting and Denial of Service (DoS) cyberattacks.
Yet a further approach to locating objects in diverse geographical areas is to use traditional ISR techniques. These techniques include using Unmanned Air Systems (UASs) for air, surface, subsurface scanning and image acquisition. However, these techniques also have their drawbacks in that they are typically associated with high costs, a short search time, difficulties in relevancy (e.g., the unmanned aerial system must be in the right area at right time), and they require large communication bandwidths to send imagery data to a ground station for processing.
One form of object detection in which targets are recognized is called Automatic Target Recognition (ATR) of which “Discrimination” is a sub-class. Edward Dou, of Raytheon presented a paper entitled “Automatic Target Recognition: Bringing Academic Progression into the Aerospace Industry,” Raytheon MEOSTN Symposium, Tucson Ariz., Jun. 8-11, 2015 in which he conducted a literature reviewed 165 academic papers, spanning journals and conferences, and concluded that there was little or no published academic research with respect to providing confidence gages (i.e. confidence intervals) with respect to the quality and accuracy of ATR and discrimination algorithm results.
For industry and the Department of Defense, traditional approaches for object detection use advanced satellite, radar, and ISR capabilities to collect and then fuse mission data (S. Levachkine, et al., “Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images,” Progress in Pattern Recognition, Speech and Image Analysis, Lecture Notes in Computer Science Vol. 2905, 2003, pp 635-642.). There are several drawbacks to this approach. For example, the ISR collection platform can add significant additional cost, may have limited search time, must be in the right area at right time, and requires additional time to complete the ISR data collection and analysis process. Other limitations to this approach include: correlation of collected data is weak, results are not statistically computed, and (as additional ISR assets and staff are applied to identify objects) redundant tasking and inefficient mission planning are likely to occur (T. Pham, et al., “Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations,” 11th Intl. Conf. Inform. Fusion, June 2008). Traditional tools that funnel data into the location and recognition information do not overcome these limitations and meet mission critical requirements (T. Pham, et al., “Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations,” 11th Intl. Conf. Inform. Fusion, June 2008 and P. Hershey and M Wang, “Composable, Distributed System to Derive Actionable Mission Information from Intelligence, Surveillance, and Reconnaissance (ISR) Data,” Proc. of the 2013 IEEE Systems Conference, Orlando, Fla., Apr. 17, 2013).